1,721,152 research outputs found
A bivariate model for improving the estimation of relative risk
No full textDisease mapping studies have been widely performed at univariate level, that is considering only one disease in the estimated models. Nonetheless, simultaneous modeling of different diseases can be a valuable tool both from the epidemiological and from the statistical point of view. In this paper we propose a model for bivariate disease mapping that generalises the univariate CAR distribution. The proposed model is proven to be an effective alternative to existing bivariate models, mainly because it overcome some restrictive hypotheses underlying models previously proposed in this context. Model performances are checked via a simulation study and via application to some real case studies
Estimation of multi-way tables subject to coherence constraints
Full text linkNowadays, traditional population censuses based on total enumeration of the population are being accompanied by sample surveys. Sampling within censuses allows to reduce costs and workload of authorities involved in censuses operations, along with the statistical burden for the people involved in the enumeration. In this paper, we deal with estimation of multi-way contingency tables involving variables measured both via census and sampling. In this framework, two main issues need to be addressed: first of all, sample size for estimating some of the entries of the contingency tables may be too small, delivering estimates prone to huge sampling variability. On the other hand, since estimates of the joint distribution need to be coherent with the marginal distribution of the variable collected via a census, estimation methods need to be coherent with the constraint imposed by marginal distribution of variables measured via census. The problem is tackled via a model-based approach that allows to comply with all coherence constraints following a fairly simple procedure. The merit of the proposed methodology is illustrated by means of a simulation study
Spatio-temporal regression on compositional covariates: modeling vegetation in a gypsum outcrop
Full text linkInvestigating the relationship between vegetation cover and substrate typologies is important for habitat conservation. To study these relationships, common
practice in modern ecological surveys is to collect information regarding vegetation cover and substrate typology over fine regular lattices, as derived from digital ground photos. Information on substrate typologies is often available as compositional measures, e.g., the area proportion occupied by a certain substrate. Two primary issues are of interest for ecologists: first, how much substrate typologies differ in terms of relative suitability for vegetation cover and, second, whether suitability varies over time. This paper develops a procedure for managing compositional covariates within a Bayesian hierarchical framework to effectively address the aforementioned issues. A spatio-temporal model is adopted to estimate the temporal pattern characterizing substrate relative suitability for vegetation cover and, at the same time, to account for spatio-temporal correlation. Relative suitability is modeled by time-varying regression coefficients, and spatial, temporal and spatio-temporal random effects are modeled using Gaussian Markov Random Field models
A novel solution for the development of a sentimental analysis chatbot integrating ChatGPT
Full text linkIn today’s business landscape, Chatbots play a pivotal role in innovation and process optimization. In this paper, we introduced a novel advanced Emotional Chatbot AI, introducing sentiment analysis for human chatbot conversations. Adding an emotional component within the human-computer interaction, can in fact dramatically improve the quality of the final conversation between Chatbots and humans. More specifically, in our paper, we provided a practical evaluation of the EmoROBERTA software, introducing it into a novel implementation of an Emotional Chatbot. The pipeline we present is novel, and we developed it within a business context in which the use of sentimental and emotional responses can act in a significant and fundamental way toward the final success and use of the Chatbot itself. The architecture enriches user experience with real-time updates on the topic of interest, maintaining a user-centric design, toward an affective-response enhancement of the interaction established between the Chatbot and the user. The source code is fully available on GitHub: https://github.com/filippoflorindi/F-One
Regression on compositional covariates: assessing substrate suitability for vegetation
No full textInvestigating the relationship between vegetation cover and substrate typologies is important in habitat conservation and management. We focus on a modern ecological survey, where information regarding vegetation cover are derived from digital ground photos taken at different times. The aim is to estimate the effect of different substrate typologies on vegetation cover (substrate suitability). As it is often the case in ground cover imaging, information on substrate typologies are available as compositional data, e.g., the area proportion occupied by a certain substrate. We develop a novel procedure for managing compositional covariates within a Bayesian hierarchical framework and illustrate it with data from a gypsum outcrop located in the Emilia Romagna region, Italy
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